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rm(list=ls()) Problem Set 5
Github Repo Link to Problem Set 5:
Problem 1 - OOP Programming
Create a class to represent rational numbers. Do this using S4.
a. For the rational class, define the following:
- A constructor 2.A validator that ensures the denominator is non-zero. 3.A show method. 4.A simplify method, to obtain the simplest form (e.g. simplify(2/4) produces 1/2). 5.A quotient method (e.g. quotient(3/7) produces .42857143…). It should support a digits argument but only in the printing, not the returned result (Hint: what does print return?). 6.Addition, subtraction, multiplication, division. These should all return a rational. 7.You’ll (probably) need GCD and LCM as part of some of these calculations; include these functions using Rcpp. Even if you don’t need these functions for another calculation, include them.
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# Load Rcpp for GCD and LCM functions
library(Rcpp)
# Define GCD and LCM functions in a single C++ code block
cppFunction("
#include <cmath> // Include cmath for abs function
#include <numeric> // Include numeric for std::gcd and std::lcm functions
int C_gcd(int x, int y) {
return std::gcd(x, y);
}")
cppFunction("
int C_lcm(int x, int y) {
return (abs(x * y) / std::gcd(x, y));
}
")Show the code
# Set up rational class
# Define the S4 class
setClass("Rational",
slots = c(numerator = "numeric", denominator = "numeric"),
prototype = list(numerator = 0, denominator = 1)
)
# Validity
setValidity("Rational", function(object) {
if (object@denominator == 0) {
return("Denominator cannot be zero.")
}
TRUE
})Class "Rational" [in ".GlobalEnv"]
Slots:
Name: numerator denominator
Class: numeric numericShow the code
# Simplify method-- this is just for internal use within constructor to obtain the simplest form
simplify_fraction <- function(numerator, denominator) {
divisor <- C_gcd(numerator, denominator)
numerator <- numerator / divisor
denominator <- denominator / divisor
list(numerator = numerator, denominator = denominator)
}
# Constructor
Rational <- function(numerator, denominator = 1) {
if (denominator == 0) {
stop("Error: Denominator cannot be zero.")
}
# Simplify the fraction upon creation
simplified <- simplify_fraction(numerator, denominator)
obj <- new("Rational", numerator = simplified$numerator, denominator = simplified$denominator)
obj # Return the valid object
}
# Show method
setMethod("show", "Rational", function(object) {
cat(object@numerator, "/", object@denominator, "\n")
})
# Simplify method for external use
setGeneric("simplify", function(object) standardGeneric("simplify"))[1] "simplify"Show the code
setMethod("simplify", "Rational", function(object) {
simplified <- simplify_fraction(object@numerator, object@denominator)
new("Rational", numerator = simplified$numerator, denominator = simplified$denominator)
})
# Quotient
setGeneric("quotient", function(object, digits = NULL) standardGeneric("quotient"))[1] "quotient"Show the code
setMethod("quotient", "Rational", function(object, digits = NULL) {
result <- object@numerator / object@denominator
if (!is.null(digits) && is.numeric(digits)) {
print(round(result, digits))
} else {
print(result)
}
invisible(result)
})
# Addition
setMethod("+", signature(e1 = "Rational", e2 = "Rational"), function(e1, e2) {
lcm_den <- C_lcm(e1@denominator, e2@denominator)
numerator <- e1@numerator * (lcm_den / e1@denominator) + e2@numerator * (lcm_den / e2@denominator)
Rational(numerator, lcm_den) # Automatically simplifies
})
# Subtraction
setMethod("-", signature(e1 = "Rational", e2 = "Rational"), function(e1, e2) {
lcm_den <- C_lcm(e1@denominator, e2@denominator)
numerator <- e1@numerator * (lcm_den / e1@denominator) - e2@numerator * (lcm_den / e2@denominator)
Rational(numerator, lcm_den) # Automatically simplifies
})
# Multiplication
setMethod("*", signature(e1 = "Rational", e2 = "Rational"), function(e1, e2) {
Rational(e1@numerator * e2@numerator, e1@denominator * e2@denominator) # Automatically simplifies
})
# Division
setMethod("/", signature(e1 = "Rational", e2 = "Rational"), function(e1, e2) {
if (e2@numerator == 0) stop("Cannot divide by a rational number with zero numerator.")
Rational(e1@numerator * e2@denominator, e1@denominator * e2@numerator) # Automatically simplifies
})b. Use your rational class to create three objects:
r1: 24/6
r2: 7/230
r3: 0/4
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# Creating objects
r1 <- Rational(24, 6) # This should simplify to 4/1
r2 <- Rational(7, 230) # This will stay as 7/230
r3 <- Rational(0, 4) # This should simplify to 0/1
r1 # 4/14 / 1 Show the code
r2 # 7/2307 / 230 Show the code
r3 #0/10 / 1 Evaluate the following code (remember you can tell Quarto not to stop on errors):
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r14 / 1 Show the code
r30 / 1 Show the code
r1 + r2927 / 230 Show the code
r1 - r2913 / 230 Show the code
r1 * r214 / 115 Show the code
r1 / r2920 / 7 Show the code
r1 + r34 / 1 Show the code
r1 * r30 / 1 Show the code
r2 / r3Error in r2/r3: Cannot divide by a rational number with zero numerator.Show the code
quotient(r1)[1] 4Show the code
quotient(r2)[1] 0.03043478Show the code
quotient(r2, digits = 3)[1] 0.03Show the code
quotient(r2, digits = 3.14)[1] 0.03Show the code
quotient(r2, digits = "avocado")[1] 0.03043478Show the code
q2 <- quotient(r2, digits = 3)[1] 0.03Show the code
q2[1] 0.03043478Show the code
quotient(r3)[1] 0Show the code
simplify(r1)4 / 1 Show the code
simplify(r2)7 / 230 Show the code
simplify(r3)0 / 1 Show that your validator does not allow the creation of rational’s with 0 denominator, and check other malformed input to your constructor.
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# Testing the validator for Rational objects with invalid inputs -- should all error
Rational(1, 0)Error in Rational(1, 0): Error: Denominator cannot be zero.Show the code
Rational("a", 2)Error in eval(expr, envir, enclos): Not compatible with requested type: [type=character; target=integer].Show the code
Rational(1, "b")Error in eval(expr, envir, enclos): Not compatible with requested type: [type=character; target=integer].Show the code
Rational("a", "b")Error in eval(expr, envir, enclos): Not compatible with requested type: [type=character; target=integer].Note that there are a lot of choices to be made here. How are you going to store the class? Two numerics? A vector of length two? A formula? A string? What are users going to pass into the constructor? A string (“24/6”)? Two arguments? A vector?
There is no right answer to those questions. Make the best decision you can, and don’t be afraid to change it if your decision causes unforeseen difficulties.
You may not use any existing R functions or packages that would trivialize this assignment. (E.g. if you found an existing package that does this, or found a function that automatically produces the quotient or simplified version, that is not able to be used.)
Hint: It may be useful to define other functions that I don’t explicitly ask for.
##Problem 2 - plotly
Let’s revisit the art data from the last problem set. Use plotly for these.
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library(plotly)Loading required package: ggplot2
Attaching package: 'plotly'The following object is masked from 'package:ggplot2':
last_plotThe following object is masked from 'package:stats':
filterThe following object is masked from 'package:graphics':
layoutShow the code
# Load art sales df
art <- read.csv("df_for_ml_improved_new_market.csv")a. Regenerate your plot which addresses the second question from last time:
ii. Does the distribution of genre of sales across years appear to change?
You may copy your plot from last time, or copy my plot from the solutions, or come up with your own new plot.
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library(tidyverse)── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ lubridate 1.9.3 ✔ tibble 3.2.1
✔ purrr 1.0.2 ✔ tidyr 1.3.1
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks plotly::filter(), stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsShow the code
# Get year and genre columns, clean up column names, and process two+ genres
genre <- art %>%
dplyr::select(year, starts_with("Genre")) %>%
rename_with(~ gsub("Genre___", "", .), starts_with("Genre___")) %>%
mutate(two_plus = rowSums(select(., Photography, Print, Sculpture, Painting, Others), na.rm = TRUE)) %>%
mutate("Two+ Genres" = ifelse(two_plus > 1, 1, 0)) %>%
select(-two_plus)
# Reshape to long format
genre_long <- genre %>%
pivot_longer(
cols = -year,
names_to = "genre",
values_to = "count"
)
# Summarize total sales for each genre per year
genre_summary <- genre_long %>%
group_by(year, genre) %>%
summarize(total_sales = sum(count, na.rm = TRUE)) %>%
ungroup() %>%
mutate(genre = factor(genre, levels = c("Photography", "Print", "Sculpture", "Painting", "Others", "Two+ Genres")))`summarise()` has grouped output by 'year'. You can override using the
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# Interactive stacked bar chart using Plotly
plot_genre_distribution <- plot_ly(
data = genre_summary,
x = ~year,
y = ~total_sales,
color = ~genre,
type = "bar",
colors = "Set3"
) %>%
layout(
title = "Distribution of Genre of Sales Across Years",
xaxis = list(title = "Year"),
yaxis = list(title = "Total Sales"),
barmode = "stack"
)
plot_genre_distributionb. Generate an interactive plot with plotly that can address both of these questions from last time:
i. Is there a change in the sales price in USD over time?
ii. How does the genre affect the change in sales price over time?
This should be a single interactive plot, with which a user can manipulate the view to be able to look at change over time overall, or by genre.
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# Calculate average sales price per genre per year
price_trend <- art %>%
dplyr::select(year, price_usd, starts_with("Genre")) %>%
# Get rid of the Genre___ start so we can just use the genre name for plot
rename_with(~ gsub("Genre___", "", .), starts_with("Genre___")) %>%
mutate(genre = case_when(Photography == 1 ~ "Photography",
Print == 1 ~ "Print",
Sculpture == 1 ~ "Sculpture",
Painting == 1 ~ "Painting",
Others == 1 ~ "Other")) %>%
group_by(year, genre) %>%
summarize(
mean_price = mean(price_usd, na.rm = TRUE),
median_price = median(price_usd, na.rm = TRUE)
) %>%
ungroup() %>%
mutate(genre = factor(genre, levels = c("Photography", "Print", "Sculpture", "Painting", "Other")))`summarise()` has grouped output by 'year'. You can override using the
`.groups` argument.Show the code
# Create an interactive plotly plot with both mean and median prices
plot <- plot_ly(data = price_trend) %>%
add_trace(
x = ~year, y = ~mean_price, color = ~genre,
type = 'scatter', mode = 'lines+markers',
name = ~paste("Mean Price -", genre),
line = list(dash = 'solid')
) %>%
add_trace(
x = ~year, y = ~median_price, color = ~genre,
type = 'scatter', mode = 'lines+markers',
name = ~paste("Median Price -", genre),
line = list(dash = 'dash')
) %>%
layout(
title = "Sales Price Over Time by Genre (Mean and Median)",
xaxis = list(title = "Year"),
yaxis = list(title = "Sales Price (USD)"),
hovermode = "closest",
legend = list(orientation = "h", y = -0.3)
)
plotThese will be graded similar to last time:
Is the type of graph & choice of variables appropriate to answer the question? Is the graph clear and easy to interpret? Is the graph publication ready?
Problem 3 - data.table
Repeat Problem 1 from PS04 using data.table.
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library(data.table)
Attaching package: 'data.table'The following objects are masked from 'package:lubridate':
hour, isoweek, mday, minute, month, quarter, second, wday, week,
yday, yearThe following objects are masked from 'package:dplyr':
between, first, lastThe following object is masked from 'package:purrr':
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library(nycflights13)a. Generate a table (which can just be a nicely printed tibble) reporting the mean and median departure delay per airport. Generate a second table (which again can be a nicely printed tibble) reporting the mean and median arrival delay per airport. Exclude any destination with under 10 flights. Do this exclusion through code, not manually.
Additionally,
Order both tables in descending mean delay. Both tables should use the airport names not the airport codes. Both tables should print all rows.
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# Departures delay
flights <- data.table(flights)
#setDT(flights)
flights[, faa := dest]Show the code
merged <- merge(flights[, faa := origin],
airports,
by = "faa",
all.x = TRUE)First table (reporting the mean and median departure delay per airport):
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merged[, .(N = .N,
mean_delay = mean(dep_delay, na.rm = TRUE),
median_delay = median(dep_delay, na.rm = TRUE)),
by = name] |>
_[N >= 10, !"N"] |>
_[order(mean_delay, decreasing = TRUE)] name mean_delay median_delay
<char> <num> <num>
1: Newark Liberty Intl 15.10795 -1
2: John F Kennedy Intl 12.11216 -1
3: La Guardia 10.34688 -3Show the code
# Departures delay
flights <- data.table(flights)
#setDT(flights)
#setDT(airports)
flights[, faa := dest]
# Arrival delay
merged <- merge(flights[, faa := dest],
airports,
by = "faa",
all.x = TRUE)Second table (reporting the mean and median arrival delay per airport):
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merged[, .(name = ifelse(is.na(first(name)), first(faa), first(name)),
N = .N,
mean_delay = mean(arr_delay, na.rm = TRUE),
median_delay = median(arr_delay, na.rm = TRUE)),
by = faa] |>
_[N >= 10, !c("faa", "N")] |>
_[order(mean_delay, decreasing = TRUE)] |>
print(x = _, nrows = 10000) name mean_delay median_delay
<char> <num> <num>
1: Columbia Metropolitan 41.76415094 28.0
2: Tulsa Intl 33.65986395 14.0
3: Will Rogers World 30.61904762 16.0
4: Jackson Hole Airport 28.09523810 15.0
5: Mc Ghee Tyson 24.06920415 2.0
6: Dane Co Rgnl Truax Fld 20.19604317 1.0
7: Richmond Intl 20.11125320 1.0
8: Akron Canton Regional Airport 19.69833729 3.0
9: Des Moines Intl 19.00573614 0.0
10: Gerald R Ford Intl 18.18956044 1.0
11: Birmingham Intl 16.87732342 -2.0
12: Theodore Francis Green State 16.23463687 1.0
13: Greenville-Spartanburg International 15.93544304 -0.5
14: Cincinnati Northern Kentucky Intl 15.36456376 -3.0
15: Savannah Hilton Head Intl 15.12950601 -1.0
16: Manchester Regional Airport 14.78755365 -3.0
17: Eppley Afld 14.69889841 -2.0
18: Yeager 14.67164179 -1.5
19: Kansas City Intl 14.51405836 0.0
20: Albany Intl 14.39712919 -4.0
21: General Mitchell Intl 14.16722038 0.0
22: Piedmont Triad 14.11260054 -2.0
23: Washington Dulles Intl 13.86420212 -3.0
24: Cherry Capital Airport 12.96842105 -10.0
25: James M Cox Dayton Intl 12.68048606 -3.0
26: Louisville International Airport 12.66938406 -2.0
27: Chicago Midway Intl 12.36422360 -1.0
28: Sacramento Intl 12.10992908 4.0
29: Jacksonville Intl 11.84483416 -2.0
30: Nashville Intl 11.81245891 -2.0
31: Portland Intl Jetport 11.66040210 -4.0
32: Greater Rochester Intl 11.56064461 -5.0
33: Hartsfield Jackson Atlanta Intl 11.30011285 -1.0
34: Lambert St Louis Intl 11.07846451 -3.0
35: Norfolk Intl 10.94909344 -4.0
36: Baltimore Washington Intl 10.72673385 -5.0
37: Memphis Intl 10.64531435 -2.5
38: Port Columbus Intl 10.60132291 -3.0
39: Charleston Afb Intl 10.59296847 -4.0
40: Philadelphia Intl 10.12719014 -3.0
41: Raleigh Durham Intl 10.05238095 -3.0
42: Indianapolis Intl 9.94043412 -3.0
43: Charlottesville-Albemarle 9.50000000 -5.0
44: Cleveland Hopkins Intl 9.18161129 -5.0
45: Ronald Reagan Washington Natl 9.06695204 -2.0
46: Burlington Intl 8.95099602 -4.0
47: Buffalo Niagara Intl 8.94595186 -5.0
48: Syracuse Hancock Intl 8.90392501 -5.0
49: Denver Intl 8.60650021 -2.0
50: Palm Beach Intl 8.56297210 -3.0
51: BQN 8.24549550 -1.0
52: Bob Hope 8.17567568 -3.0
53: Fort Lauderdale Hollywood Intl 8.08212154 -3.0
54: Bangor Intl 8.02793296 -9.0
55: Asheville Regional Airport 8.00383142 -1.0
56: PSE 7.87150838 0.0
57: Pittsburgh Intl 7.68099053 -5.0
58: Gallatin Field 7.60000000 -2.0
59: NW Arkansas Regional 7.46572581 -2.0
60: Tampa Intl 7.40852503 -4.0
61: Charlotte Douglas Intl 7.36031885 -3.0
62: Minneapolis St Paul Intl 7.27016886 -5.0
63: William P Hobby 7.17618819 -4.0
64: Bradley Intl 7.04854369 -10.0
65: San Antonio Intl 6.94537178 -9.0
66: South Bend Rgnl 6.50000000 -3.5
67: Louis Armstrong New Orleans Intl 6.49017497 -6.0
68: Key West Intl 6.35294118 7.0
69: Eagle Co Rgnl 6.30434783 -4.0
70: Austin Bergstrom Intl 6.01990875 -5.0
71: Chicago Ohare Intl 5.87661475 -8.0
72: Orlando Intl 5.45464309 -5.0
73: Detroit Metro Wayne Co 5.42996346 -7.0
74: Portland Intl 5.14157973 -5.0
75: Nantucket Mem 4.85227273 -3.0
76: Wilmington Intl 4.63551402 -7.0
77: Myrtle Beach Intl 4.60344828 -13.0
78: Albuquerque International Sunport 4.38188976 -5.5
79: George Bush Intercontinental 4.24079040 -5.0
80: Norman Y Mineta San Jose Intl 3.44817073 -7.0
81: Southwest Florida Intl 3.23814963 -5.0
82: San Diego Intl 3.13916574 -5.0
83: Sarasota Bradenton Intl 3.08243131 -5.0
84: Metropolitan Oakland Intl 3.07766990 -9.0
85: General Edward Lawrence Logan Intl 2.91439222 -9.0
86: San Francisco Intl 2.67289152 -8.0
87: SJU 2.52052659 -6.0
88: Yampa Valley 2.14285714 2.0
89: Phoenix Sky Harbor Intl 2.09704733 -6.0
90: Montrose Regional Airport 1.78571429 -10.5
91: Los Angeles Intl 0.54711094 -7.0
92: Dallas Fort Worth Intl 0.32212685 -9.0
93: Miami Intl 0.29905978 -9.0
94: Mc Carran Intl 0.25772849 -8.0
95: Salt Lake City Intl 0.17625459 -8.0
96: Long Beach -0.06202723 -10.0
97: Martha\\\\'s Vineyard -0.28571429 -11.0
98: Seattle Tacoma Intl -1.09909910 -11.0
99: Honolulu Intl -1.36519258 -7.0
100: STT -3.83590734 -9.0
101: John Wayne Arpt Orange Co -7.86822660 -11.0
102: Palm Springs Intl -12.72222222 -13.5
name mean_delay median_delayb. How many flights did the aircraft model with the fastest average speed take? Produce a tibble with 1 row, and entires for the model, average speed (in MPH) and number of flights.
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planes <- data.table(planes)
#setDT(planes)Show the code
merged <- merge(flights,
planes,
by = "tailnum",
all.x = TRUE)Show the code
allmodels <- merged[, `:=`(num_flights = .N,
avg_mph = mean(distance/(air_time/60), na.rm = TRUE)),
by = model]
allmodels[allmodels[, .I[which.max(avg_mph)]],.(model, avg_mph, num_flights)] model avg_mph num_flights
<char> <num> <int>
1: 777-222 482.6254 4